From a7a88714d8d183c75104084b984593e60b8c6097 Mon Sep 17 00:00:00 2001
From: Sophie Schoenmeyer <107952697+sophies927@users.noreply.github.com>
Date: Thu, 27 Jun 2024 20:30:37 +0000
Subject: [PATCH 1/2] Fix typo in Python code block on home page

---
 src/routes/components/code-blocks.svelte | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/routes/components/code-blocks.svelte b/src/routes/components/code-blocks.svelte
index fd51f4292e2dd..f092f899e63cd 100644
--- a/src/routes/components/code-blocks.svelte
+++ b/src/routes/components/code-blocks.svelte
@@ -11,7 +11,7 @@
 	import github from "svelte-highlight/styles/github";
 
 	let pythonCode =
-		'import onnxruntime as ort\n# Load the model and create InferenceSession\nmodel_path = "path/to/your/onnx/model"\nsession = ort.InferenceSession(model_path)\n# "Load and preprocess the input image inputTensor"\n...\n# Run inference\noutputs = session.run(None {"input": inputTensor})\nprint(outputs)';
+		'import onnxruntime as ort\n# Load the model and create InferenceSession\nmodel_path = "path/to/your/onnx/model"\nsession = ort.InferenceSession(model_path)\n# "Load and preprocess the input image inputTensor"\n...\n# Run inference\noutputs = session.run(None, {"input": inputTensor})\nprint(outputs)';
 	let csharpCode =
 		'using Microsoft.ML.OnnxRuntime;\n// Load the model and create InferenceSession\nstring model_path = "path/to/your/onnx/model";\nvar session = new InferenceSession(model_path);\n// Load and preprocess the input image to inputTensor\n...\n// Run inference\nvar outputs = session.Run(inputTensor).ToList();\nConsole.WriteLine(outputs[0].AsTensor()[0]);';
 	let javascriptCode =

From 59419029f66321e8065c14261810ce40603e3cf6 Mon Sep 17 00:00:00 2001
From: Sophie Schoenmeyer <107952697+sophies927@users.noreply.github.com>
Date: Thu, 27 Jun 2024 20:47:30 +0000
Subject: [PATCH 2/2] Fix broken mobilenet_v2 download link

---
 docs/tutorials/mobile/deploy-android.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tutorials/mobile/deploy-android.md b/docs/tutorials/mobile/deploy-android.md
index 80d0d9b82567b..af7a5621aeabf 100644
--- a/docs/tutorials/mobile/deploy-android.md
+++ b/docs/tutorials/mobile/deploy-android.md
@@ -38,7 +38,7 @@ The pre-trained [TorchVision MOBILENET V2](https://pytorch.org/hub/pytorch_visio
    - Quantize the FP32 ONNX model to an uint8 ONNX model
    - Convert both FP32 and uint8 ONNX models to ORT models
 
-   Note: this step is optional, you can download the FP32 and uint8 ORT models [here](https://onnxruntimeexamplesdata.z13.web.core.windows.net/mobilenet_v2_ort_models.zip).
+   Note: this step is optional, you can download the FP32 and uint8 ORT models [here](https://github.com/onnx/models/tree/main/validated/vision/classification/mobilenet/model).
 
 2. Download the model class labels